Authors:
Rosario Di Lascio
1
;
Pasquale Foggia
2
;
Alessia Saggese
2
and
Mario Vento
2
Affiliations:
1
A.I.Tech s.r.l., Italy
;
2
University of Salerno, Italy
Keyword(s):
Video Surveillance, Real-time Object Tracking.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
In this paper we propose a novel real-time tracking algorithm robust with respect to several common errors occurring in object detection systems, especially in the presence of total or partial occlusions. The algorithm takes into account the history of each object, whereas most other methods base their decisions on only the last few frames. More precisely, it associates each object with a state encoding the relevant information of its past history, that enable the most appropriate way of assigning an identity to the object on the basis of its current and past conditions. Thus, strategies that are more complex but also riskier are only applied when the algorithm is confident that is appropriate to do so. An experimental evaluation of the algorithm has been performed using the PETS2010 database, comparing the obtained performance with the results of the PETS 2010 contest participants.